【作者】 李桂娥；

【导师】 李畅；

【作者基本信息】 华中师范大学 ， 地图学与地理信息系统， 2017， 硕士

【摘要】 随着LBS(Location-basedService)的快速发展,室内定位商业化需求骤增,零售业巨头如梅西百货等开始在店内部署iBeacon信标。但就当前而言,室内定位精度和算法复杂度以及定位成本等之间的正相关关系仍然制约着室内定位主流技术的确定和广泛应用。因此,本文针对低能耗蓝牙提出了一种知识引导下逐级进行信号源优选的室内定位精度优化方法。本文所做的研究与工作如下:(1)先验与后验知识库的建立与运用。先验知识库的内容和实现的作用主要包括:1)不同介质的路径损耗系数,可用于信号传播衰减模型距离的估算;2)室内场景三维地图,一方面用于对室内场景进行可视域分析和遮挡情况的判别,另一方面用于室内空间约束和路网约束;3)蓝牙信号源的基本情况,可用于定位方案选取和定位结果优化。后验知识库的内容则主要包括,通过获取蓝牙信号强度估算定位点对蓝牙信号进行一定程度的补偿并应用于以后的定位中;此外,还包括通过对定位数据进行分析,从而对路径损耗系数进行一定修正,进而对定位进行优化。(2)逐级信号源优选与定位算法优化,提高蓝牙信号源的使用效率,提高定位精度。首先利用室内三维地图进行可视域分析,判别区域内信号源遮挡情况,根据定位需求对遮挡信号源进行选择性使用,并同时确定遮挡物的路径损耗系数,以此实现对信号源的初选。在三维定位解算阶段,本文在二维算法的基础上,提出了三种针对不同室内情况的备选方案,即基于泰勒级数的三维定位算法、基于稳健回归估计的三维室内定位算法和基于随机抽样一致性检验的室内定位算法。(3)本文将上述算法和思想融合,提出了基于信号源优选的室内定位方案。基于前期的调查研究工作,本文基于Matlab实现了定位算法的运行,并基于智能手机实现了定位方案中的部分关键模块。(4)本文对定位解算阶段提出的算法在Matlab中进行了仿真测试,对三种定位算法的适用范围给出了评价,实验表明本文提出的定位算法可以通过剔除信号源异常值提高定位的精度。同时,本文在已经搭建好的室内实际场景中借助信号采集APP进行了数据采集和测试,通过可视域分析、蓝牙信号源信号强度补偿、室内路径约束等进行了采样点的定位估算,并进行了数据分析和讨论,经本方法优化后在模拟数据和实际场景中的单点定位精度皆得到了明显提升。更多还原

【Abstract】 With the rapid development of LBS(Location-based Service),the demand for commercialization of indoor location has increased,and retail giants such as Macy’s have begun to deploy beacons in stores.But for the time being,the positive correlation between indoor location accuracy and the complexity of the algorithm,as well as the positioning cost is still restricting the determination and application of mainstream positioning technology.Therefore,this paper proposes a method of knowledge-based optimization of indoor location based on Bluetooth.The research and work done in this paper are as follows:(1)The establishment and application of a priori and posterior knowledge base.The contents and implementation of the prior knowledge base include:1)The path loss coefficient of different media can be used to estimate the distance of the signal propagation attenuation model.2)The indoor 3D map can be used for the identification of visibility analysis(VA)and occlusion of the indoor scene as well as for the indoor space constraints and road network constraints.3)Bluetooth signal source of the basic situation can be used to locate the program selection and positioning results optimization.The contents of the posterior knowledge base include,for example,obtaining a certain degree of compensation for the Bluetooth signal by obtaining the Bluetooth signal strength estimation and applying it to the subsequent positioning.In addition,it also includes the analysis of the positioning data by analyzing the path loss coefficient to be fixed,and then to optimize the indoor location.(2)The step-by-step optimization of the signal source and the optimization of the location algorithm to improve the efficiency of the Bluetooth signal source and improve the positioning accuracy.First,the indoor 3D map for VA can determine the area of the signal source occlusion,selective use of shielding signal source according to the positioning requirements,and at the same time determine the path loss coefficient of occlusion,in order to achieve the signal source is selected for the first time.In the three-dimensional positioning solution phase,three kinds of three-dimensional localization algorithm based on Taylor series and RANSAC(random sample consensus)are proposed under the base of the two-dimensional algorithm.(3)This paper realizes an indoor location program based on the optimized selection of the signal source.Based on the previous research work,this paper realizes the location algorithm based on Matlab,and realizes some key modules in the indoor location scheme based on the smart phone.(4)In this paper,the algorithm proposed in the algorithm of position finding is simulated by Matlab.The evaluation scope of the three kinds of positioning algorithms is given.The experimental results show that the proposed algorithm can eliminate the signal source of outliers and improve the location accuracy.At the same time,this paper carries out data acquisition by APP and test by means of signal acquisition through VA,in the built actual scene.The location estimation of the sampling points is carried out by VA,Bluetooth signal source signal strength compensation,indoor path constraint and so on.The results show that the proposed method significantly improves the single point positioning accuracy of the simulation data and the actual scene data.更多还原